Trace open source data. Forge what's missing.
TraceForge maps publicly available datasets, traces their provenance, and synthesizes high-fidelity training data that's too expensive, restricted, or impossible to collect directly.
Scan Datasets Now →From public data to private advantage
Three steps between open source datasets and production-ready training data.
Discover
Index and catalog open source datasets across repositories, papers, and public APIs.
Trace
Map provenance, lineage, and licensing. Know exactly where every data point originated.
Forge
Synthesize new training sets seeded by open source patterns. High fidelity, full traceability.
Provenance-first architecture
Every synthetic data point links back to its open source seed. Full audit trail from source to output. EU AI Act compliant by design.
Domain-specific synthesis
Target verticals where data is scarce: healthcare imaging, autonomous driving edge cases, financial fraud patterns, rare event detection.
License-aware sourcing
Automatically filters open source datasets by license compatibility. No more legal ambiguity about what you can use for commercial training.
Edge-case amplification
Open source data contains rare signals buried in noise. TraceForge identifies these patterns and amplifies them into statistically valid training sets.
The gap nobody fills
Gretel synthesizes but doesn't trace sources. Relyance traces but doesn't synthesize. MIT's Data Provenance Initiative audits but doesn't generate. TraceForge is the first platform purpose-built to do both: trace the lineage of open source data and use it to forge the training sets you actually need.
The best AI models will be built on data you can prove.
Training data scarcity isn't a technical limitation. It's a provenance problem. When you can trace what exists publicly and synthesize what doesn't, the bottleneck disappears. That's the future TraceForge is building.